Overview

Dataset statistics

Number of variables17
Number of observations182
Missing cells421
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.3 KiB
Average record size in memory136.7 B

Variable types

Numeric12
Unsupported2
Categorical3

Alerts

country has a high cardinality: 182 distinct valuesHigh cardinality
rw_country_code has a high cardinality: 179 distinct valuesHigh cardinality
rw_country_name has a high cardinality: 179 distinct valuesHigh cardinality
df_index is highly overall correlated with cartodb_idHigh correlation
cartodb_id is highly overall correlated with df_indexHigh correlation
cri_rank is highly overall correlated with cri_score and 8 other fieldsHigh correlation
cri_score is highly overall correlated with cri_rank and 8 other fieldsHigh correlation
fatalities_per_100k_rank is highly overall correlated with cri_rank and 7 other fieldsHigh correlation
fatalities_per_100k_total is highly overall correlated with cri_rank and 7 other fieldsHigh correlation
fatalities_rank is highly overall correlated with cri_rank and 7 other fieldsHigh correlation
fatalities_total is highly overall correlated with cri_rank and 7 other fieldsHigh correlation
losses_per_gdp__rank is highly overall correlated with cri_rank and 8 other fieldsHigh correlation
losses_per_gdp__total is highly overall correlated with cri_rank and 4 other fieldsHigh correlation
losses_usdm_ppp_rank is highly overall correlated with cri_rank and 8 other fieldsHigh correlation
losses_usdm_ppp_total is highly overall correlated with cri_rank and 8 other fieldsHigh correlation
the_geom has 182 (100.0%) missing valuesMissing
the_geom_webmercator has 182 (100.0%) missing valuesMissing
losses_per_gdp__total has 51 (28.0%) missing valuesMissing
rw_country_code has 3 (1.6%) missing valuesMissing
rw_country_name has 3 (1.6%) missing valuesMissing
df_index is uniformly distributedUniform
cartodb_id is uniformly distributedUniform
country is uniformly distributedUniform
rw_country_code is uniformly distributedUniform
rw_country_name is uniformly distributedUniform
df_index has unique valuesUnique
cartodb_id has unique valuesUnique
country has unique valuesUnique
the_geom is an unsupported type, check if it needs cleaning or further analysisUnsupported
the_geom_webmercator is an unsupported type, check if it needs cleaning or further analysisUnsupported
fatalities_per_100k_total has 70 (38.5%) zerosZeros
fatalities_total has 69 (37.9%) zerosZeros
losses_usdm_ppp_total has 48 (26.4%) zerosZeros

Reproduction

Analysis started2023-02-10 03:18:49.263574
Analysis finished2023-02-10 03:19:51.863907
Duration1 minute and 2.6 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.5
Minimum0
Maximum181
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:52.640227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.05
Q145.25
median90.5
Q3135.75
95-th percentile171.95
Maximum181
Range181
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation52.683014
Coefficient of variation (CV)0.58213276
Kurtosis-1.2
Mean90.5
Median Absolute Deviation (MAD)45.5
Skewness0
Sum16471
Variance2775.5
MonotonicityStrictly increasing
2023-02-10T04:19:52.844679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.5%
114 1
 
0.5%
116 1
 
0.5%
117 1
 
0.5%
118 1
 
0.5%
119 1
 
0.5%
120 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
Other values (172) 172
94.5%
ValueCountFrequency (%)
0 1
0.5%
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
ValueCountFrequency (%)
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%
177 1
0.5%
176 1
0.5%
175 1
0.5%
174 1
0.5%
173 1
0.5%
172 1
0.5%

cartodb_id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.5
Minimum1
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:53.075064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91.5
Q3136.75
95-th percentile172.95
Maximum182
Range181
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation52.683014
Coefficient of variation (CV)0.57577065
Kurtosis-1.2
Mean91.5
Median Absolute Deviation (MAD)45.5
Skewness0
Sum16653
Variance2775.5
MonotonicityStrictly increasing
2023-02-10T04:19:53.332376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
115 1
 
0.5%
117 1
 
0.5%
118 1
 
0.5%
119 1
 
0.5%
120 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
Other values (172) 172
94.5%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%
177 1
0.5%
176 1
0.5%
175 1
0.5%
174 1
0.5%
173 1
0.5%

the_geom
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing182
Missing (%)100.0%
Memory size1.5 KiB

the_geom_webmercator
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing182
Missing (%)100.0%
Memory size1.5 KiB

country
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Saudi Arabia
 
1
Papua New Guinea
 
1
Peru
 
1
Pakistan
 
1
Panama
 
1
Other values (177)
177 

Length

Max length37
Median length28
Mean length9.2142857
Min length4

Characters and Unicode

Total characters1677
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)100.0%

Sample

1st rowSaudi Arabia
2nd rowRomania
3rd rowSpain
4th rowSlovenia
5th rowSouth Sudan

Common Values

ValueCountFrequency (%)
Saudi Arabia 1
 
0.5%
Papua New Guinea 1
 
0.5%
Peru 1
 
0.5%
Pakistan 1
 
0.5%
Panama 1
 
0.5%
Paraguay 1
 
0.5%
Republic of Congo 1
 
0.5%
Republic of Yemen 1
 
0.5%
Senegal 1
 
0.5%
Russia 1
 
0.5%
Other values (172) 172
94.5%

Length

2023-02-10T04:19:53.815087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 13
 
5.3%
of 7
 
2.8%
and 5
 
2.0%
united 3
 
1.2%
democratic 3
 
1.2%
guinea 2
 
0.8%
st 2
 
0.8%
new 2
 
0.8%
congo 2
 
0.8%
islands 2
 
0.8%
Other values (202) 205
83.3%

Most occurring characters

ValueCountFrequency (%)
a 239
 
14.3%
i 145
 
8.6%
e 119
 
7.1%
n 118
 
7.0%
o 93
 
5.5%
r 87
 
5.2%
u 69
 
4.1%
l 66
 
3.9%
64
 
3.8%
t 58
 
3.5%
Other values (45) 619
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1372
81.8%
Uppercase Letter 235
 
14.0%
Space Separator 64
 
3.8%
Other Punctuation 3
 
0.2%
Dash Punctuation 2
 
0.1%
Final Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 239
17.4%
i 145
10.6%
e 119
 
8.7%
n 118
 
8.6%
o 93
 
6.8%
r 87
 
6.3%
u 69
 
5.0%
l 66
 
4.8%
t 58
 
4.2%
s 51
 
3.7%
Other values (16) 327
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 25
 
10.6%
B 19
 
8.1%
R 18
 
7.7%
M 17
 
7.2%
C 17
 
7.2%
A 14
 
6.0%
G 13
 
5.5%
T 13
 
5.5%
I 12
 
5.1%
L 12
 
5.1%
Other values (14) 75
31.9%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
' 1
33.3%
Space Separator
ValueCountFrequency (%)
64
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1607
95.8%
Common 70
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 239
14.9%
i 145
 
9.0%
e 119
 
7.4%
n 118
 
7.3%
o 93
 
5.8%
r 87
 
5.4%
u 69
 
4.3%
l 66
 
4.1%
t 58
 
3.6%
s 51
 
3.2%
Other values (40) 562
35.0%
Common
ValueCountFrequency (%)
64
91.4%
. 2
 
2.9%
- 2
 
2.9%
’ 1
 
1.4%
' 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676
99.9%
Punctuation 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 239
 
14.3%
i 145
 
8.7%
e 119
 
7.1%
n 118
 
7.0%
o 93
 
5.5%
r 87
 
5.2%
u 69
 
4.1%
l 66
 
3.9%
64
 
3.8%
t 58
 
3.5%
Other values (44) 618
36.9%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

cri_rank
Real number (ℝ)

Distinct122
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.230769
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:54.032536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91
Q3135
95-th percentile135
Maximum135
Range134
Interquartile range (IQR)88.75

Descriptive statistics

Standard deviation44.708529
Coefficient of variation (CV)0.52455855
Kurtosis-1.2929055
Mean85.230769
Median Absolute Deviation (MAD)44
Skewness-0.34713452
Sum15512
Variance1998.8525
MonotonicityNot monotonic
2023-02-10T04:19:54.295832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 48
26.4%
113 2
 
1.1%
105 2
 
1.1%
8 2
 
1.1%
98 2
 
1.1%
91 2
 
1.1%
87 2
 
1.1%
124 2
 
1.1%
53 2
 
1.1%
44 2
 
1.1%
Other values (112) 116
63.7%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 2
1.1%
10 1
0.5%
11 1
0.5%
ValueCountFrequency (%)
135 48
26.4%
134 1
 
0.5%
133 1
 
0.5%
132 1
 
0.5%
131 1
 
0.5%
130 1
 
0.5%
129 1
 
0.5%
128 1
 
0.5%
127 1
 
0.5%
126 1
 
0.5%

cri_score
Real number (ℝ)

Distinct122
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.791923
Minimum12.17
Maximum124.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:55.000191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.17
5-th percentile25.32
Q152.8725
median77.5
Q3124.5
95-th percentile124.5
Maximum124.5
Range112.33
Interquartile range (IQR)71.6275

Descriptive statistics

Standard deviation34.582412
Coefficient of variation (CV)0.42280962
Kurtosis-1.2487351
Mean81.791923
Median Absolute Deviation (MAD)31.25
Skewness-0.15607203
Sum14886.13
Variance1195.9432
MonotonicityNot monotonic
2023-02-10T04:19:55.259530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124.5 48
26.4%
98.33 2
 
1.1%
93.17 2
 
1.1%
23.33 2
 
1.1%
86 2
 
1.1%
77.5 2
 
1.1%
76.17 2
 
1.1%
105.83 2
 
1.1%
57 2
 
1.1%
52.17 2
 
1.1%
Other values (112) 116
63.7%
ValueCountFrequency (%)
12.17 1
0.5%
13 1
0.5%
13.83 1
0.5%
15.33 1
0.5%
20.33 1
0.5%
20.83 1
0.5%
22.83 1
0.5%
23.33 2
1.1%
25.17 1
0.5%
28.17 1
0.5%
ValueCountFrequency (%)
124.5 48
26.4%
121.5 1
 
0.5%
117.33 1
 
0.5%
117 1
 
0.5%
114.33 1
 
0.5%
110.5 1
 
0.5%
110.33 1
 
0.5%
109.33 1
 
0.5%
109.17 1
 
0.5%
106 1
 
0.5%

fatalities_per_100k_rank
Real number (ℝ)

Distinct114
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.60989
Minimum1
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:55.498891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91.5
Q3114
95-th percentile114
Maximum114
Range113
Interquartile range (IQR)67.75

Descriptive statistics

Standard deviation37.858511
Coefficient of variation (CV)0.48159985
Kurtosis-1.1042709
Mean78.60989
Median Absolute Deviation (MAD)22.5
Skewness-0.60499466
Sum14307
Variance1433.2669
MonotonicityNot monotonic
2023-02-10T04:19:55.736263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 69
37.9%
18 1
 
0.5%
113 1
 
0.5%
77 1
 
0.5%
59 1
 
0.5%
91 1
 
0.5%
11 1
 
0.5%
44 1
 
0.5%
96 1
 
0.5%
38 1
 
0.5%
Other values (104) 104
57.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
114 69
37.9%
113 1
 
0.5%
112 1
 
0.5%
111 1
 
0.5%
110 1
 
0.5%
109 1
 
0.5%
108 1
 
0.5%
107 1
 
0.5%
106 1
 
0.5%
105 1
 
0.5%

fatalities_per_100k_total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51967033
Minimum0
Maximum43.66
Zeros70
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:55.951679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.12
95-th percentile0.9845
Maximum43.66
Range43.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation3.3939343
Coefficient of variation (CV)6.5309372
Kurtosis146.49494
Mean0.51967033
Median Absolute Deviation (MAD)0.02
Skewness11.664636
Sum94.58
Variance11.51879
MonotonicityNot monotonic
2023-02-10T04:19:56.160121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 70
38.5%
0.02 13
 
7.1%
0.01 9
 
4.9%
0.03 7
 
3.8%
0.11 6
 
3.3%
0.04 6
 
3.3%
0.08 5
 
2.7%
0.06 5
 
2.7%
0.05 4
 
2.2%
0.1 4
 
2.2%
Other values (39) 53
29.1%
ValueCountFrequency (%)
0 70
38.5%
0.01 9
 
4.9%
0.02 13
 
7.1%
0.03 7
 
3.8%
0.04 6
 
3.3%
0.05 4
 
2.2%
0.06 5
 
2.7%
0.07 3
 
1.6%
0.08 5
 
2.7%
0.09 3
 
1.6%
ValueCountFrequency (%)
43.66 1
0.5%
9.07 1
0.5%
8.74 1
0.5%
5.19 1
0.5%
4.09 1
0.5%
3.66 1
0.5%
1.77 1
0.5%
1.25 1
0.5%
1.14 1
0.5%
0.99 1
0.5%

fatalities_rank
Real number (ℝ)

Distinct64
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.626374
Minimum1
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:56.432420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median90
Q3114
95-th percentile114
Maximum114
Range113
Interquartile range (IQR)67.75

Descriptive statistics

Standard deviation37.500688
Coefficient of variation (CV)0.48309211
Kurtosis-1.1070012
Mean77.626374
Median Absolute Deviation (MAD)24
Skewness-0.56860796
Sum14128
Variance1406.3016
MonotonicityNot monotonic
2023-02-10T04:19:56.658755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 69
37.9%
102 12
 
6.6%
90 12
 
6.6%
60 5
 
2.7%
85 5
 
2.7%
81 4
 
2.2%
57 3
 
1.6%
65 3
 
1.6%
68 3
 
1.6%
78 3
 
1.6%
Other values (54) 63
34.6%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
114 69
37.9%
102 12
 
6.6%
90 12
 
6.6%
85 5
 
2.7%
81 4
 
2.2%
78 3
 
1.6%
76 2
 
1.1%
74 2
 
1.1%
72 2
 
1.1%
71 1
 
0.5%

fatalities_total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.840659
Minimum0
Maximum4317
Zeros69
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:57.227238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q322.75
95-th percentile263.55
Maximum4317
Range4317
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation427.35062
Coefficient of variation (CV)4.9210891
Kurtosis72.005821
Mean86.840659
Median Absolute Deviation (MAD)2
Skewness8.1959733
Sum15805
Variance182628.55
MonotonicityNot monotonic
2023-02-10T04:19:57.505525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69
37.9%
1 12
 
6.6%
2 12
 
6.6%
12 5
 
2.7%
3 5
 
2.7%
4 4
 
2.2%
13 3
 
1.6%
11 3
 
1.6%
10 3
 
1.6%
5 3
 
1.6%
Other values (54) 63
34.6%
ValueCountFrequency (%)
0 69
37.9%
1 12
 
6.6%
2 12
 
6.6%
3 5
 
2.7%
4 4
 
2.2%
5 3
 
1.6%
6 2
 
1.1%
7 2
 
1.1%
8 2
 
1.1%
9 1
 
0.5%
ValueCountFrequency (%)
4317 1
0.5%
3336 1
0.5%
1663 1
0.5%
916 1
0.5%
410 1
0.5%
364 1
0.5%
351 1
0.5%
325 1
0.5%
288 1
0.5%
267 1
0.5%

losses_per_gdp__rank
Real number (ℝ)

Distinct135
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.302198
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:57.768822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91.5
Q3135
95-th percentile135
Maximum135
Range134
Interquartile range (IQR)88.75

Descriptive statistics

Standard deviation44.6916
Coefficient of variation (CV)0.52392085
Kurtosis-1.2931559
Mean85.302198
Median Absolute Deviation (MAD)43.5
Skewness-0.3487033
Sum15525
Variance1997.3391
MonotonicityNot monotonic
2023-02-10T04:19:58.025136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 48
 
26.4%
131 1
 
0.5%
58 1
 
0.5%
103 1
 
0.5%
55 1
 
0.5%
88 1
 
0.5%
79 1
 
0.5%
27 1
 
0.5%
119 1
 
0.5%
60 1
 
0.5%
Other values (125) 125
68.7%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
135 48
26.4%
134 1
 
0.5%
133 1
 
0.5%
132 1
 
0.5%
131 1
 
0.5%
130 1
 
0.5%
129 1
 
0.5%
128 1
 
0.5%
127 1
 
0.5%
126 1
 
0.5%

losses_per_gdp__total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct125
Distinct (%)95.4%
Missing51
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean1.5176542
Minimum0.0001
Maximum77.3694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:58.371217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.00085
Q10.0165
median0.0841
Q30.27595
95-th percentile1.52185
Maximum77.3694
Range77.3693
Interquartile range (IQR)0.25945

Descriptive statistics

Standard deviation8.1712673
Coefficient of variation (CV)5.384143
Kurtosis62.861875
Mean1.5176542
Median Absolute Deviation (MAD)0.0781
Skewness7.5840832
Sum198.8127
Variance66.769609
MonotonicityNot monotonic
2023-02-10T04:19:58.664427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0001 2
 
1.1%
0.0006 2
 
1.1%
0.0012 2
 
1.1%
0.0088 2
 
1.1%
0.008 2
 
1.1%
0.0419 2
 
1.1%
0.0974 1
 
0.5%
0.0989 1
 
0.5%
0.4262 1
 
0.5%
0.4154 1
 
0.5%
Other values (115) 115
63.2%
(Missing) 51
28.0%
ValueCountFrequency (%)
0.0001 2
1.1%
0.0002 1
0.5%
0.0003 1
0.5%
0.0004 1
0.5%
0.0006 2
1.1%
0.0011 1
0.5%
0.0012 2
1.1%
0.002 1
0.5%
0.0021 1
0.5%
0.0025 1
0.5%
ValueCountFrequency (%)
77.3694 1
0.5%
40.6504 1
0.5%
33.3333 1
0.5%
12.5786 1
0.5%
6.25 1
0.5%
4.4507 1
0.5%
1.5444 1
0.5%
1.4993 1
0.5%
1.0945 1
0.5%
0.9044 1
0.5%

losses_usdm_ppp_rank
Real number (ℝ)

Distinct135
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.302198
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:58.961601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91.5
Q3135
95-th percentile135
Maximum135
Range134
Interquartile range (IQR)88.75

Descriptive statistics

Standard deviation44.6916
Coefficient of variation (CV)0.52392085
Kurtosis-1.2931559
Mean85.302198
Median Absolute Deviation (MAD)43.5
Skewness-0.3487033
Sum15525
Variance1997.3391
MonotonicityNot monotonic
2023-02-10T04:19:59.334635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 48
 
26.4%
119 1
 
0.5%
35 1
 
0.5%
116 1
 
0.5%
55 1
 
0.5%
37 1
 
0.5%
58 1
 
0.5%
64 1
 
0.5%
107 1
 
0.5%
24 1
 
0.5%
Other values (125) 125
68.7%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
135 48
26.4%
134 1
 
0.5%
133 1
 
0.5%
132 1
 
0.5%
131 1
 
0.5%
130 1
 
0.5%
129 1
 
0.5%
128 1
 
0.5%
127 1
 
0.5%
126 1
 
0.5%

losses_usdm_ppp_total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct135
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907.5076
Minimum0
Maximum40077.222
Zeros48
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-02-10T04:19:59.791383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.347
Q3262.17825
95-th percentile2836.6696
Maximum40077.222
Range40077.222
Interquartile range (IQR)262.17825

Descriptive statistics

Standard deviation4483.9985
Coefficient of variation (CV)4.9410038
Kurtosis58.981872
Mean907.5076
Median Absolute Deviation (MAD)30.347
Skewness7.5841349
Sum165166.38
Variance20106243
MonotonicityNot monotonic
2023-02-10T04:20:00.079608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
26.4%
1.229 1
 
0.5%
385.632 1
 
0.5%
2.649 1
 
0.5%
201.805 1
 
0.5%
333.205 1
 
0.5%
161.838 1
 
0.5%
112.043 1
 
0.5%
7.322 1
 
0.5%
907.122 1
 
0.5%
Other values (125) 125
68.7%
ValueCountFrequency (%)
0 48
26.4%
0.025 1
 
0.5%
0.027 1
 
0.5%
0.087 1
 
0.5%
0.114 1
 
0.5%
0.13 1
 
0.5%
0.136 1
 
0.5%
0.166 1
 
0.5%
0.253 1
 
0.5%
0.276 1
 
0.5%
ValueCountFrequency (%)
40077.222 1
0.5%
36272.535 1
0.5%
27122.7 1
0.5%
4186.23 1
0.5%
4174.851 1
0.5%
3812.502 1
0.5%
3427.958 1
0.5%
2894.407 1
0.5%
2869.197 1
0.5%
2838.711 1
0.5%

rw_country_code
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct179
Distinct (%)100.0%
Missing3
Missing (%)1.6%
Memory size1.5 KiB
SAU
 
1
RUS
 
1
PRT
 
1
PER
 
1
PAK
 
1
Other values (174)
174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)100.0%

Sample

1st rowSAU
2nd rowROU
3rd rowESP
4th rowSVN
5th rowSSD

Common Values

ValueCountFrequency (%)
SAU 1
 
0.5%
RUS 1
 
0.5%
PRT 1
 
0.5%
PER 1
 
0.5%
PAK 1
 
0.5%
PAN 1
 
0.5%
PRY 1
 
0.5%
COG 1
 
0.5%
YEM 1
 
0.5%
SEN 1
 
0.5%
Other values (169) 169
92.9%
(Missing) 3
 
1.6%

Length

2023-02-10T04:20:00.358861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sau 1
 
0.6%
gha 1
 
0.6%
swz 1
 
0.6%
esp 1
 
0.6%
svn 1
 
0.6%
ssd 1
 
0.6%
sle 1
 
0.6%
zaf 1
 
0.6%
srb 1
 
0.6%
svk 1
 
0.6%
Other values (169) 169
94.4%

Most occurring characters

ValueCountFrequency (%)
A 42
 
7.8%
R 41
 
7.6%
N 38
 
7.1%
M 33
 
6.1%
L 31
 
5.8%
B 30
 
5.6%
S 30
 
5.6%
T 28
 
5.2%
G 27
 
5.0%
E 25
 
4.7%
Other values (16) 212
39.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 537
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 42
 
7.8%
R 41
 
7.6%
N 38
 
7.1%
M 33
 
6.1%
L 31
 
5.8%
B 30
 
5.6%
S 30
 
5.6%
T 28
 
5.2%
G 27
 
5.0%
E 25
 
4.7%
Other values (16) 212
39.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 537
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 42
 
7.8%
R 41
 
7.6%
N 38
 
7.1%
M 33
 
6.1%
L 31
 
5.8%
B 30
 
5.6%
S 30
 
5.6%
T 28
 
5.2%
G 27
 
5.0%
E 25
 
4.7%
Other values (16) 212
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 42
 
7.8%
R 41
 
7.6%
N 38
 
7.1%
M 33
 
6.1%
L 31
 
5.8%
B 30
 
5.6%
S 30
 
5.6%
T 28
 
5.2%
G 27
 
5.0%
E 25
 
4.7%
Other values (16) 212
39.5%

rw_country_name
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct179
Distinct (%)100.0%
Missing3
Missing (%)1.6%
Memory size1.5 KiB
Saudi Arabia
 
1
Russia
 
1
Portugal
 
1
Peru
 
1
Pakistan
 
1
Other values (174)
174 

Length

Max length32
Median length24
Mean length8.8379888
Min length4

Characters and Unicode

Total characters1582
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)100.0%

Sample

1st rowSaudi Arabia
2nd rowRomania
3rd rowSpain
4th rowSlovenia
5th rowSouth Sudan

Common Values

ValueCountFrequency (%)
Saudi Arabia 1
 
0.5%
Russia 1
 
0.5%
Portugal 1
 
0.5%
Peru 1
 
0.5%
Pakistan 1
 
0.5%
Panama 1
 
0.5%
Paraguay 1
 
0.5%
Republic of Congo 1
 
0.5%
Yemen 1
 
0.5%
Senegal 1
 
0.5%
Other values (169) 169
92.9%
(Missing) 3
 
1.6%

Length

2023-02-10T04:20:00.649085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 7
 
3.0%
of 6
 
2.6%
and 5
 
2.1%
united 4
 
1.7%
saint 3
 
1.3%
south 3
 
1.3%
guinea 3
 
1.3%
the 3
 
1.3%
sudan 2
 
0.9%
congo 2
 
0.9%
Other values (194) 197
83.8%

Most occurring characters

ValueCountFrequency (%)
a 238
15.0%
i 138
 
8.7%
n 121
 
7.6%
e 112
 
7.1%
o 83
 
5.2%
r 83
 
5.2%
t 62
 
3.9%
u 61
 
3.9%
56
 
3.5%
l 56
 
3.5%
Other values (42) 572
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1302
82.3%
Uppercase Letter 223
 
14.1%
Space Separator 56
 
3.5%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 238
18.3%
i 138
10.6%
n 121
9.3%
e 112
 
8.6%
o 83
 
6.4%
r 83
 
6.4%
t 62
 
4.8%
u 61
 
4.7%
l 56
 
4.3%
d 50
 
3.8%
Other values (16) 298
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 27
 
12.1%
B 19
 
8.5%
C 17
 
7.6%
M 17
 
7.6%
A 15
 
6.7%
G 13
 
5.8%
T 12
 
5.4%
L 12
 
5.4%
I 11
 
4.9%
R 11
 
4.9%
Other values (14) 69
30.9%
Space Separator
ValueCountFrequency (%)
56
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1525
96.4%
Common 57
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 238
15.6%
i 138
 
9.0%
n 121
 
7.9%
e 112
 
7.3%
o 83
 
5.4%
r 83
 
5.4%
t 62
 
4.1%
u 61
 
4.0%
l 56
 
3.7%
d 50
 
3.3%
Other values (40) 521
34.2%
Common
ValueCountFrequency (%)
56
98.2%
- 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 238
15.0%
i 138
 
8.7%
n 121
 
7.6%
e 112
 
7.1%
o 83
 
5.2%
r 83
 
5.2%
t 62
 
3.9%
u 61
 
3.9%
56
 
3.5%
l 56
 
3.5%
Other values (42) 572
36.2%

Interactions

2023-02-10T04:19:44.052980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:04.408897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:12.427058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:15.059162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:17.368053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:20.245625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:22.987677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:27.004268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:30.471648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:34.843056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:37.924811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:40.679057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:44.383105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:05.296826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:12.597616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:15.353419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:17.596444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:20.478278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:23.285895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:27.279648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:30.729544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:35.108342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:38.113307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:40.911374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:44.669331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:05.507262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:12.810028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:15.572829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:17.804884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:20.653723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:23.565686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:27.561951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:31.803180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:35.431477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:38.342201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:41.264397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:44.944594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:05.786376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:12.997582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:15.757321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:17.986461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:20.837233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:23.794123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:27.800300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:32.134300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:35.678821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:38.559620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:41.602491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:45.224845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:05.978894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:13.190013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:15.936808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:18.149962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:21.007841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:24.146264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:28.093563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:32.527244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:35.884267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:38.791999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:41.883739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:45.624776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:06.259460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:13.393613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:16.097413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:18.441185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:21.177493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:24.526248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:28.470562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:32.810498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:36.112655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:39.018394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:42.148032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:45.962877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:06.480867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:13.583134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:16.291931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:18.681555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:21.382009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:24.896263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:28.737817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:33.059857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:36.386924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:39.268724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:42.427293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:46.402699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:06.668341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:13.743679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:16.489402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:18.931871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:21.574495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:25.287391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:28.985837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:33.370988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:36.621296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:39.554321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:42.702551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:46.731818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:06.851900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:13.910278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:16.641995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:19.138442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:21.768975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:25.711432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:29.279572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:33.660215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:36.855669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:39.784666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:42.930938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:47.031016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:11.502495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:14.090785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:16.812545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:19.405872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:21.991473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:26.081937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:29.570741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:33.918560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:37.105002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:40.044969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:43.227187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:47.311267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:11.801693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:14.372051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:16.992059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:19.630270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:22.319032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:26.419099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:29.855620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:34.168855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:37.399216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:40.278345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:43.527424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:47.834872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:12.065987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:14.733028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:17.156618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:19.836718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:22.679064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:26.727182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:30.122943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:34.498971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:37.671491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:40.487536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T04:19:43.781705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-10T04:20:01.199190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
df_indexcartodb_idcri_rankcri_scorefatalities_per_100k_rankfatalities_per_100k_totalfatalities_rankfatalities_totallosses_per_gdp__ranklosses_per_gdp__totallosses_usdm_ppp_ranklosses_usdm_ppp_total
df_index1.0001.0000.1020.1020.067-0.0720.150-0.1500.0830.1260.192-0.192
cartodb_id1.0001.0000.1020.1020.067-0.0720.150-0.1500.0830.1260.192-0.192
cri_rank0.1020.1021.0001.0000.853-0.8540.828-0.8280.894-0.7210.864-0.864
cri_score0.1020.1021.0001.0000.853-0.8540.828-0.8280.894-0.7210.864-0.864
fatalities_per_100k_rank0.0670.0670.8530.8531.000-0.9980.906-0.9060.591-0.1830.568-0.568
fatalities_per_100k_total-0.072-0.072-0.854-0.854-0.9981.000-0.9060.906-0.5920.182-0.5700.570
fatalities_rank0.1500.1500.8280.8280.906-0.9061.000-1.0000.531-0.0340.673-0.673
fatalities_total-0.150-0.150-0.828-0.828-0.9060.906-1.0001.000-0.5310.034-0.6730.673
losses_per_gdp__rank0.0830.0830.8940.8940.591-0.5920.531-0.5311.000-1.0000.851-0.851
losses_per_gdp__total0.1260.126-0.721-0.721-0.1830.182-0.0340.034-1.0001.000-0.6070.607
losses_usdm_ppp_rank0.1920.1920.8640.8640.568-0.5700.673-0.6730.851-0.6071.000-1.000
losses_usdm_ppp_total-0.192-0.192-0.864-0.864-0.5680.570-0.6730.673-0.8510.607-1.0001.000

Missing values

2023-02-10T04:19:48.451217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-10T04:19:49.171291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-10T04:19:51.101259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexcartodb_idthe_geomthe_geom_webmercatorcountrycri_rankcri_scorefatalities_per_100k_rankfatalities_per_100k_totalfatalities_rankfatalities_totallosses_per_gdp__ranklosses_per_gdp__totallosses_usdm_ppp_ranklosses_usdm_ppp_totalrw_country_coderw_country_name
001NaNNaNSaudi Arabia7972.50180.45181401310.00011191.229SAUSaudi Arabia
112NaNNaNRomania6161.501120.011021160.6746112797.884ROURomania
223NaNNaNSpain6966.33740.054722860.039431637.070ESPSpain
334NaNNaNSlovenia135124.501140.001140135NaN1350.000SVNSlovenia
445NaNNaNSouth Sudan133117.331140.0011401200.00211220.508SSDSouth Sudan
556NaNNaNSierra Leone10288.50420.1668101240.00111310.114SLESierra Leone
667NaNNaNSouth Africa3345.67840.035119240.472273427.958ZAFSouth Africa
778NaNNaNSerbia8375.501140.001140330.279445272.927SRBRepublic of Serbia
889NaNNaNSlovak Republic123105.33970.0210211150.00461067.468SVKSlovakia
9910NaNNaNSolomon Islands8976.83390.171021800.04451210.511SLBSolomon Islands
df_indexcartodb_idthe_geomthe_geom_webmercatorcountrycri_rankcri_scorefatalities_per_100k_rankfatalities_per_100k_totalfatalities_rankfatalities_totallosses_per_gdp__ranklosses_per_gdp__totallosses_usdm_ppp_ranklosses_usdm_ppp_totalrw_country_coderw_country_name
172172173NaNNaNMontenegro135124.501140.001140135NaN1350.000MNEMontenegro
173173174NaNNaNRwanda11097.00600.0968101300.00011340.025RWARwanda
174174175NaNNaNQatar129110.331140.0011401130.00609419.273QATQatar
175175176NaNNaNPuerto Rico11097.001140.001140810.04377857.565NaNNaN
176176177NaNNaNSamoa135124.501140.001140135NaN1350.000WSMSamoa
177177178NaNNaNSeychelles135124.501140.001140135NaN1350.000SYCSeychelles
178178179NaNNaNGambia135124.501140.001140135NaN1350.000GMBGambia
179179180NaNNaNTogo131114.331040.0110211230.00121300.130TGOTogo
180180181NaNNaNTrinidad and Tobago135124.501140.001140135NaN1350.000TTOTrinidad and Tobago
181181182NaNNaNTonga135124.501140.001140135NaN1350.000TONTonga